Bill Aulwes: Welcome to Always Savvy with Bill Aulwes. I'm a financial advisor at UBS in Phoenix, Arizona. This podcast is your guide for meaningful conversations with leaders across a variety of industries.
The format is simple. Our goal is getting you the most interesting information in just a few minutes. We keep it short, but still want you to be savvy.
Welcome back to Always Savvy. I'm here with Kevin Dennean today, who is an equity strategist following technology. Thanks for joining me here in New York.
Kevin Dennean: Great to be with you, Bill.
Bill Aulwes: You're actually my first podcast, so I really appreciate you coming back.
Kevin Dennean: I'm honored that you had me back.
Bill Aulwes: So let's start with maybe what's the elephant in the room. Is there an AI bubble?
Kevin Dennean: Yeah, I think it's absolutely the place we have to start with whenever we start talking about AI, especially now that we're coming up on the three-year anniversary of the launch of ChatGPT and three years of significant market activity. I think that we're not in a bubble. I think if you look at the history of financial markets, bubbles come, they happen, they eventually burst, but I think bubbles always have had three characteristics in financial markets.
The first is a bubble is created on the back of a new technology or a new innovation, So we can check that box, right? Clearly, even if AI is arguably 40, 50 years old, large language models were a completely new innovation that really, I think, moved the needle here.
The second thing that we look at when we look for bubbles is we have that new technology and innovation, and that drives a significant increase in asset prices or equity values. We can check that box. But this is where the argument for a bubble breaks down. That increase in asset values or equity values, it has to be driven by valuation increases. That can't be justified by fundamentals.
Now, I'm the first one to tell you that valuation is much more art than science, but if we just look at two things, if we look at some of the leading companies in AI, some of the companies that are widely viewed as being AI winners or leveraged to AI, and you decompose their returns over the last few years, it's actually been pretty balanced between earnings growth and valuation expansion.
There has been some multiple increases. Those multiple increases that increase in valuation, it didn't happen in a vacuum. We can look at accelerating earnings growth, we can look at improved returns on equity. There's things that you can anchor it to.
The other point that I would make is, let's just look at the numbers. If we look at, say, some of the AI leaders today, I'm not going to argue that the valuations are cheap. They're trading roughly, on average, as a group, about 35 times. But let's compare and contrast that to the internet bubble. So, when you think back, and you remember we used to talk about The Four Horsemen of the Internet?
Bill Aulwes: Mm-hmm.
Kevin Dennean: As a group, those stocks were trading at 65 times forward earnings. So today, the stocks that we worry about being in a bubble are at a significant discount to stocks that we know ex-posts were in a bubble. And another point that I would argue is the quality of the E in P, the quality of earnings is significantly different today.
We look at straight gap earnings. The market, investors aren't doing that pro forma adjustment, which is fancy speak for, "Let's take out everything that makes the numbers look bad," right? These are high-quality earnings numbers on high-quality businesses, with some of the best balance sheets in the world, healthy margins, strong cash flow, good management teams. We just don't think you can make that bubble argument right now.
Now, there may be some AI-related stocks that we can find in isolation that are in a bubble, or maybe there's some other thing out there related to AI that I don't particularly traffic in that could be a bubble. But I don't think when we look at technology stocks in a public market, I don't think we would argue that there is a bubble. Could it turn into a bubble? Sure, it could. Time will tell, but right now we think valuations are pretty well grounded in fundamentals.
Bill Aulwes: Okay, good. Thank you for that. I remember I started my career during the bubble. It was 1995, mutual fund company, we actually didn't have a fund that was invested in that, and it served us pretty well in the end, in hindsight.
Kevin Dennean: Yeah.
Bill Aulwes: So, okay, not a bubble, but we saw some large deals announced between a leading AI company and some semiconductor companies. Brings up maybe bad memories of vendor financing from the dot-com era and concerns over circularity and concentration risk. What are your thoughts around that?
Kevin Dennean: So, I started covering technology stocks in the late '90s, so I was right there with you during the dot-com era. I'm always on a lookout for warning signs and red flags. I do understand sort of the hair trigger warning that we all want to take this for, but I think there are some differences.
So, I think from when our team inside of CIO of UBS Wealth Management, when we wrote our first big white paper on AI, one of the things we said is that AI will be the land of giants. Scale matters a lot in AI. You need scaled up capital to build the data centers. You need scaled up data to train the models on.
I think this is really just another reflection of that. I think that these deals are large in size, but I do understand the circularity argument, but I think it's that it requires such scaled up capital, there's not that many other sources.
In terms of vendor financing, I think there are some definite differences. So, a lot of the recently announced deals, they have milestones attached to them. They're not just simply blank checks like we saw telecom equipment companies writing to second, third, fourth rate telecom companies. So that's really a difference.
They're also equity rather than debt. The numbers are large, but when we do the math, I think the deals in aggregate account for maybe a low double-digit percentage of what we expect to be spent on AI compute capacity over the next 3, 4, 5 years.
So what that means, is there's roughly 90% of all the investments coming from stuff away from these so-called circular deals, there's going to be external capital raises. Obviously, private equity has a big role to play in this.
But getting back to the land of giants, the biggest companies that are making these investments are also the large technology companies that generate enormous amounts of cash flow, and they're putting that cash flow to work.
Bill Aulwes: Okay, great. So how should clients be thinking about investing in AI?
Kevin Dennean: So the first thing I would say is we do think that AI should have an allocation in your equity portfolio. The second thing I would say is that every client should talk to their advisor to understand just how much that exposure should be.
I think when we get down to putting money to work across the AI complex, I think the framework to look at it is we see AI as a new technology stack, and what I mean by a technology stack is think of a layer cake, and we see AI as having three layers. First is the enabling layer. That's where all the compute is, the storage. And when I say compute, I mean the GPUs and the ASICs, all the things that do all the calculations on a model.
That's where the storage is, the networking, the data centers, the power, the cooling, the telecom connections that come into it, internet connections, fiber connections, all that sort of stuff. You can think of that as almost like the physical layer.
Above that, you have what we call the intelligence layer. That's where the models live, and that's where the data lives. That's also where a lot of the cloud service providers live. At the very top of the stack or the top layer of the cake, if you want to think of it that way, is the application layer, application services layer. That's where you and I and everyone else will interact with AI, and that's where eventually AI agents will interact with other AI agents.
So that's the framework that we see, and we think clients should have exposure across all three pieces of that stack. Ultimately, we think that value will accrete over time more and more towards those upper layers, but right now, obviously, there's a lot of growth in that bottom layer because of all the investments that's being made, and we have a lot of visibility into that growth, but we think you should have exposure across all three layers.
Bill Aulwes: Okay. Fantastic. Thank you. Always a great summary on this part of the market with you. I will leave you with this. I used ChatGPT for my packing list for my trip to New York on this trip, so it actually worked out really well. I'm trying to get more into using AI as part of daily life.
Kevin Dennean: Yeah, I think that's actually one of the biggest challenges, is that I think it's about individuals, businesses, enterprises, we've got to adapt our workflow. I think that's actually one of the things that we're hearing more consistently. It's that people want to use it. They're still trying to figure out how to fit it in.
Bill Aulwes: Yep. It's still early days.
Kevin Dennean: It absolutely is.
Bill Aulwes: All right. Thank you, Kevin. Appreciate you being here.
Kevin Dennean: Thanks, Bill.
Bill Aulwes: My name is Bill Aulwes. Feel free to reach out with any questions about your financial needs. You can reach me at william.aulwes@ubs.com. Aulwes is spelled A-U-L-W-E-S, as in Sam. Have a great day.
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